Reversion to the Mean is extreme results tend to be followed by less extreme ones. It sits in the Cognition dimension (COG) of the Human Behavior Taxonomy™ as element HBT-COG-0584, within the Probability family. The core principle: extreme results tend to be followed by less extreme ones. In incentive terms, it matters because it changes the payoff people perceive before they choose — which means it can be designed for, or exploited.
Scientific Definition
Extreme results tend to be followed by less extreme ones.
Plain-English Definition
Extreme results tend to be followed by less extreme ones.
Feynman Explanation
The hot hand and the cold streak both end.
Core Principle
Extreme results tend to be followed by less extreme ones.
Mechanisms
Pending editorial review.
Extreme results tend to be followed by less extreme ones.
Pending editorial review.
Pending editorial review.
Pending editorial review.
Pending editorial review.
Don't overweight outliers in either direction.
Inputs (Triggers)
Pending editorial review.
Outputs (Behaviors)
Pending editorial review.
Behavioral Signature
The hot hand and the cold streak both end.
Examples
- Performance management. Investment returns.
- Don't overweight outliers in either direction.
Pending editorial review.
Original analysis from The Incentives Lab — how this element behaves inside real payoff structures.
Why this element matters to incentive design
Executives usually notice this element only after it has cost something. By then it looks like a one-off. It is not. The mechanism underneath it is straightforward: extreme results tend to be followed by less extreme ones. You can recognize it in the field by its signature: the hot hand and the cold streak both end. Every element in the Cognition dimension changes the perceived payoff of an action before the action happens, which is exactly where incentive design has leverage.
How it gets exploited
Left undesigned, don't overweight outliers in either direction. It is amplified whenever don't overweight outliers in either direction. Inside organizations that shows up as don't overweight outliers in either direction. The pattern is the same one Goodhart's Law describes: the measurable proxy attracts the effort, and the purpose behind it quietly loses funding.
How the Lab designs around it
The redesign move is to look at rolling averages, not point-in-time peaks or troughs. Watch for it at the boundaries: handoffs, promotions, incident reviews, and budget cycles are where this element gets its power.
Famous Experiments
Pending editorial review.
Design Principles
- Look at rolling averages, not point-in-time peaks or troughs.
Measurement Approaches
Pending editorial review.
Evidence
Pending editorial review.
Pending editorial review.
The Perverse Incentive Lens™
How this behavior is exploited — and how to redesign around it.
- Look at rolling averages, not point-in-time peaks or troughs.
Pending editorial review.
Pending editorial review.
Interactive Mini Network
Click any neighbor to re-center the graph and follow the threads of connection.
Knowledge Graph Neighbors
Auto-linked to the rest of the Human Behavior Taxonomy by family, domain, dimension, and shared keywords.
Extreme outcomes tend to be followed by more average ones.
Ignoring general statistics in favor of specific, vivid information.
Posterior = (likelihood × prior) / evidence.
Update beliefs in proportion to the strength of new evidence.
Start with a prior; update with new evidence.
Novices experiencing early success, often due to variance and small samples.
High-impact, hard-to-predict, retrospectively explainable events.
Striking pattern that is statistically expected in large samples.
Reasoning in distributions — ranges and probabilities — rather than points.
Average outcomes across the population differ from outcomes across time for one person.
A rough calculation using order-of-magnitude reasoning.
In quantum mechanics, certain pairs of properties cannot both be precisely known.
Where Reversion to the Mean is cited in the corpus
Essays, field guides, and diagnostics from The Incentives Lab that apply this element.
- EssayGoodhart's Law in the Real World
How measurable proxies capture judgment.
- EssayThe Perverse Incentives Hiding in Your KPIs
Cognitive shortcuts turned into scorecards.
- EssayAI Agents Inherit Your Incentives
How this element propagates into automated systems.
- ReferenceThe incentive glossary
Definitions for every mental model, bias, and fallacy in the corpus.
- ReferenceThe Periodic Table of Human Behavior
The full 1,267-element map this page belongs to.
Questions about Reversion to the Mean
- What is Reversion to the Mean?
- Reversion to the Mean is extreme results tend to be followed by less extreme ones. It sits in the Cognition dimension (COG) of the Human Behavior Taxonomy™ as element HBT-COG-0584, within the Probability family. The core principle: extreme results tend to be followed by less extreme ones. In incentive terms, it matters because it changes the payoff people perceive before they choose — which means it can be designed for, or exploited.
- What is an example of Reversion to the Mean?
- Don't overweight outliers in either direction. The Incentives Lab catalogs everyday, organizational, and historical instances of this element on its Human Behavior Taxonomy™ page (HBT-COG-0584).
- How is Reversion to the Mean exploited?
- Don't overweight outliers in either direction.
- How do you design around Reversion to the Mean?
- Look at rolling averages, not point-in-time peaks or troughs.
- Which behavioral dimension does Reversion to the Mean belong to?
- Reversion to the Mean is classified in the Cognition dimension (COG) of the Human Behavior Taxonomy™, family "Probability", class "Mental Model". Its permanent identifier is HBT-COG-0584 and its evidence grade is B.